Urologists regularly develop clinical risk prediction models to support clinical decisions. In contrast to traditional performance measures, decision curve analysis (DCA) can assess the utility of ...models for decision making. DCA plots net benefit (NB) at a range of clinically reasonable risk thresholds.
To provide recommendations on interpreting and reporting DCA when evaluating prediction models.
We informally reviewed the urological literature to determine investigators’ understanding of DCA. To illustrate, we use data from 3616 patients to develop risk models for high-grade prostate cancer (n=313, 9%) to decide who should undergo a biopsy. The baseline model includes prostate-specific antigen and digital rectal examination; the extended model adds two predictors based on transrectal ultrasound (TRUS).
We explain risk thresholds, NB, default strategies (treat all, treat no one), and test tradeoff. To use DCA, first determine whether a model is superior to all other strategies across the range of reasonable risk thresholds. If so, that model appears to improve decisions irrespective of threshold. Second, consider if there are important extra costs to using the model. If so, obtain the test tradeoff to check whether the increase in NB versus the best other strategy is worth the additional cost. In our case study, addition of TRUS improved NB by 0.0114, equivalent to 1.1 more detected high-grade prostate cancers per 100 patients. Hence, adding TRUS would be worthwhile if we accept subjecting 88 patients to TRUS to find one additional high-grade prostate cancer or, alternatively, subjecting 10 patients to TRUS to avoid one unnecessary biopsy.
The proposed guidelines can help researchers understand DCA and improve application and reporting.
Decision curve analysis can identify risk models that can help us make better clinical decisions. We illustrate appropriate reporting and interpretation of decision curve analysis.
Decision curve analysis (DCA) is a method to evaluate whether risk prediction models can have utility for supporting treatment decisions. This guide for researchers explains what DCA is, how to interpret it, and how to report its results.
Mass loss is one of the key parameters that determine stellar evolution. Despite the progress we have achieved over the last decades we still cannot match the observational derived values with ...theoretical predictions. Even worse, there are certain phases, such as the Be supergiants (BeSGs) and the Luminous Blue Variables (LBVs), where significant mass is lost through episodic or outburst activity. This leads to various structures forming around them that permit dust formation, making these objects bright IR sources. The ASSESS project aims to determine the role of episodic mass in the evolution of massive stars, by examining large numbers of cool and hot objects (such as BeSGs/LBVs). For this purpose, we initiated a large observation campaign to obtain spectroscopic data for ∼1000 IR-selected sources in 27 nearby galaxies. Within this project we successfully identified seven Be supergiants (one candidate) and four Luminous Blue Variables of which six and two, respectively, are new discoveries. We used spectroscopic, photometric, and light curve information to better constrain the nature of the reported objects. We particularly noted the presence of BeSGs at metallicity environments as low as 0.14 Z⊙.
We suggest an adaptive sample size calculation method for developing clinical prediction models, in which model performance is monitored sequentially as new data comes in.
We illustrate the approach ...using data for the diagnosis of ovarian cancer (n = 5914, 33% event fraction) and obstructive coronary artery disease (CAD; n = 4888, 44% event fraction). We used logistic regression to develop a prediction model consisting only of a priori selected predictors and assumed linear relations for continuous predictors. We mimicked prospective patient recruitment by developing the model on 100 randomly selected patients, and we used bootstrapping to internally validate the model. We sequentially added 50 random new patients until we reached a sample size of 3000 and re-estimated model performance at each step. We examined the required sample size for satisfying the following stopping rule: obtaining a calibration slope ≥ 0.9 and optimism in the c-statistic (or AUC) < = 0.02 at two consecutive sample sizes. This procedure was repeated 500 times. We also investigated the impact of alternative modeling strategies: modeling nonlinear relations for continuous predictors and correcting for bias on the model estimates (Firth's correction).
Better discrimination was achieved in the ovarian cancer data (c-statistic 0.9 with 7 predictors) than in the CAD data (c-statistic 0.7 with 11 predictors). Adequate calibration and limited optimism in discrimination was achieved after a median of 450 patients (interquartile range 450-500) for the ovarian cancer data (22 events per parameter (EPP), 20-24) and 850 patients (750-900) for the CAD data (33 EPP, 30-35). A stricter criterion, requiring AUC optimism < = 0.01, was met with a median of 500 (23 EPP) and 1500 (59 EPP) patients, respectively. These sample sizes were much higher than the well-known 10 EPP rule of thumb and slightly higher than a recently published fixed sample size calculation method by Riley et al. Higher sample sizes were required when nonlinear relationships were modeled, and lower sample sizes when Firth's correction was used.
Adaptive sample size determination can be a useful supplement to fixed a priori sample size calculations, because it allows to tailor the sample size to the specific prediction modeling context in a dynamic fashion.
The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature.
We conducted a Medline literature ...search (1/2016 to 8/2017) and extracted comparisons between LR and ML models for binary outcomes.
We included 71 of 927 studies. The median sample size was 1,250 (range 72–3,994,872), with 19 predictors considered (range 5–563) and eight events per predictor (range 0.3–6,697). The most common ML methods were classification trees, random forests, artificial neural networks, and support vector machines. In 48 (68%) studies, we observed potential bias in the validation procedures. Sixty-four (90%) studies used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was not addressed in 56 (79%) studies. We identified 282 comparisons between an LR and ML model (AUC range, 0.52–0.99). For 145 comparisons at low risk of bias, the difference in logit(AUC) between LR and ML was 0.00 (95% confidence interval, −0.18 to 0.18). For 137 comparisons at high risk of bias, logit(AUC) was 0.34 (0.20–0.47) higher for ML.
We found no evidence of superior performance of ML over LR. Improvements in methodology and reporting are needed for studies that compare modeling algorithms.
We aimed to explore the underlying reasons that estimates of overdiagnosis vary across and within low‐dose computed tomography (LDCT) lung cancer screening trials. We conducted a systematic review to ...identify estimates of overdiagnosis from randomised controlled trials of LDCT screening. We then analysed the association of Ps (the excess incidence of lung cancer as a proportion of screen‐detected cases) with postscreening follow‐up time using a linear random effects meta‐regression model. Separately, we analysed annual Ps estimates from the US National Lung Screening Trial (NLST) and German Lung Cancer Screening Intervention Trial (LUSI) using exponential decay models with asymptotes. We conducted stratified analyses to investigate participant characteristics associated with Ps using the extended follow‐up data from NLST. Among 12 overdiagnosis estimates from 8 trials, the postscreening follow‐up ranged from 3.8 to 9.3 years, and Ps ranged from −27.0% (ITALUNG, 8.3 years follow‐up) to 67.2% (DLCST, 5.0 years follow‐up). Across trials, 39.1% of the variation in Ps was explained by postscreening follow‐up time. The annual changes in Ps were −3.5% and −3.9% in the NLST and LUSI trials, respectively. Ps was predicted to plateau at 2.2% for NLST and 9.2% for LUSI with hypothetical infinite follow‐up. In NLST, Ps increased with age from −14.9% (55‐59 years) to 21.7% (70‐74 years), and time trends in Ps varied by histological type. The findings suggest that differences in postscreening follow‐up time partially explain variation in overdiagnosis estimates across lung cancer screening trials. Estimates of overdiagnosis should be interpreted in the context of postscreening follow‐up and population characteristics.
What's new?
Estimates of the magnitude of overdiagnosis of lung cancer from low‐dose computed tomography (LDCT) vary greatly, raising questions about whether the benefits of LDCT for lung cancer screening outweigh potential risks. This systematic review of randomised controlled trials of LDCT in lung cancer screening shows that 39% of variation in estimates of overdiagnosis is associated with postscreening follow‐up time. Estimates of overdiagnosis differed by population characteristics, and over time, trends in incidence varied by histological type. By facilitating a more nuanced understanding of overdiagnosis, the findings could help enable more effective communication with patients about screening benefits and harms.
We aimed to explore the underlying reasons that estimates of overdiagnosis vary across and within low-dose CT (LDCT) lung cancer screening trials. We conducted a systematic review to identify ...estimates of overdiagnosis from randomised controlled trials of LDCT screening. We then analysed the association of Ps (the excess incidence of lung cancer as a proportion of screen-detected cases) with post-screening follow-up time using a linear random effects meta-regression model. Separately, we analysed annual Ps estimates from the US National Lung Screening Trial (NLST) and German Lung Cancer Screening Intervention Trial (LUSI) using exponential decay models with asymptotes. We conducted stratified analyses to investigate participant characteristics associated with Ps using the extended follow-up data from NLST. Among 12 overdiagnosis estimates from 8 trials, the post-screening follow-up ranged from 3.8 to 9.3 years, and Ps ranged from −27.0% (ITALUNG, 8.3y follow-up) to 67.2% (DLCST, 5.0y follow-up). Across trials, 39.1% of the variation in Ps was explained by post-screening follow-up time. The annual changes in Ps were −3.5% and −3.9% in the NLST and LUSI trials, respectively. Ps was predicted to plateau at 2.2% for NLST and 9.2% for LUSI with hypothetical infinite follow-up. In NLST, Ps increased with age from −14.9% (55–59 years) to 21.7% (70–74 years), and time trends in Ps varied by histological type. The findings suggest that differences in post-screening follow-up time partially explain variation in overdiagnosis estimates across lung cancer screening trials. Estimates of overdiagnosis should be interpreted in the context of post-screening follow-up and population characteristics.
The button sequestrum sign is demonstrated in a number of medical conditions and refers to a lesion of devascularised bone which is surrounded by lucency. Although it may be difficult to arrive at a ...single diagnosis based on this sign, the combination of clinical and paraclinical findings, patient's medical history and imagistic presentation of this sign can lead to a great specificity in chronic osteomyelitis, even if osteomyelitis is accompanied by osteopetrosis, as in the present case.
The current paper focuses on a trial to understand the imaging manifestations in combination with the clinical presentation of the sacrococcygeal chordoma in a patient with referred back pain. Also, ...the steps for the final diagnosis are described and via this procedure, the paper demonstrates the crucial role of magnetic resonance imaging, computed tomography guided biopsy and histopathological examination in order to minimize the differential diagnosis and lead to the correct diagnosis.
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![Discovering New B[e] Superg...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/2075-4434/small "Discovering New B[e] Supergiants and Candidate Luminous Blue Variables in Nearby Galaxies")
